Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach

2021-12-17 · Daofei Li, Ao Liu

To develop driving automation technologies for human, a human-centered methodology should be adopted for ensured safety and satisfactory user experience. Automated lane change decision in dense highway traffic is challenging, especially when considering the personalized preferences of different drivers. To fulfill human driver centered decision algorithm development, we carry out driver-in-the-loop experiments on a 6-Degree-of-Freedom driving simulator. Based on the analysis of the lane change data by drivers of three specific styles,personalization indicators are selected to describe the driver preferences in lane change decision. Then a deep reinforcement learning (RL) approach is applied to design human-like agents for automated lane change decision, with refined reward and loss functions to capture the driver preferences.The trained RL agents and benchmark agents are tested in a two-lane highway driving scenario, and by comparing the agents with the specific drivers at the same initial states of lane change, the statistics show that the proposed algorithm can guarantee higher consistency of lane change decision preferences. The driver personalization indicators and the proposed RL-based lane change decision algorithm are promising to contribute in automated lane change system developing.

📄 PDF Abstract BibTeX arXiv:2112.13646

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle

2025-09-01 · Ziteng Gao, Jiaqi Qu, Chaoyu Chen arxiv

Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change d…

Reinforcement Learning Trajectory Prediction Autonomous Vehicles

Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning

2020-10-29 · Zhuoxi Liu, Zheng Wang, Bo Yang, Kimihiko Nakano

In this article, the authors present a novel method to learn the personalized tactic of discretionary lane-change initiation for fully autonomous vehicles through human-computer interactions. Instead of learning from hum…

Autonomous Driving Autonomous Vehicles Reinforcement Learning (RL)

Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change

2024-03-01 · Ruichen Xu, Xiao Liu, Jinming Xu, Yuan Lin

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in compl…

Autonomous Driving Autonomous Vehicles reinforcement-learning

PALCAS: A Priority-Aware Intelligent Lane Change Advisory System for Autonomous Vehicles using Federated Reinforcement Learning

2026-04-29 · Yassine Ibork, Nhat Ha Nguyen, Myounggyu Won, Lokesh Das arxiv

We present a priority-aware intelligent lane change advisory system based on multi-agent federated reinforcement learning, namely PALCAS, for autonomous vehicles (AVs). While existing lane-change approaches typically foc…

Reinforcement Learning Autonomous Vehicles

Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

2022-07-01 · Jingda Wu, Wenhui Huang, Niels de Boer, Yanghui Mo 외

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address …

Autonomous Driving Autonomous Vehicles Decision Making Reinforcement Learning (RL)